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Spatiotemporal Land-Use Dynamics in Continental Portugal 1995–2018
Publication . Alves, André; Marcelino, Filipe; Gomes, Eduardo; Rocha, Jorge; Caetano, Mário; Information Management Research Center (MagIC) - NOVA Information Management School; NOVA Information Management School (NOVA IMS); Molecular Diversity Preservation International (MDPI)
Monitoring land-use patterns and its trends provides useful information for impact evaluation and policy design. The latest in-depth studies of land-use dynamics for continental Portugal are outdated, and have not examined how municipalities may be classified into a typology of observed dynamics or considered the trajectory profiles of land-use transitions. This paper presents a comprehensive analysis of the spatiotemporal dynamics of land-use in continental Portugal from 1995 to 2018. Our multi-scalar approach used land-use maps in geographic information systems with the following objectives: (i) quantify variations of land-use classes, (ii) assess the transitions between uses, and (iii) derive a municipal typology of land-use dynamics. The methodology employed involved calculating statistical indicators of land-use classes, transition matrices between uses and combinatorial analysis for the most common trajectory-profiles. For the typology, a principal component analysis was used for dimensionality reduction and the respective components were classified by testing several clustering techniques. Results showed that the land-use transitions were not homogeneous in space or time, leading to the growth of territorial asymmetries. Forest (Δ5%), water bodies (Δ28%) and artificial surfaces (Δ35%) had a greater expansion, as opposed to agricultural areas, which had the biggest decline (Δ-8%). Despite the decline of agricultural activities, olive-grove expansion (Δ7%) was a relevant dynamic, and in the case of forests, the increment of eucalyptus (Δ34%) replaced native species such as the maritime pine (Δ-20%). A land-use-dynamics typology was estimated, dividing continental Portugal into 11 clusters, which is informative for sectoral policies and spatial planning, as zonings in need of interventions tailored to their specificities. The findings are a contribution to the study of land-use dynamics in continental Portugal, presenting various challenges for sustainable land uses with regard to the urban system, forest management, food production, soil preservation, and ecosystem protection.
Spatially Stratified and Multi-Stage Approach for National Land Cover Mapping Based on Sentinel-2 Data and Expert Knowledge
Publication . Costa, Hugo; Benevides, Pedro; Moreira, Francisco D.; Moraes, Daniel; Caetano, Mário; NOVA Information Management School (NOVA IMS); Information Management Research Center (MagIC) - NOVA Information Management School; Molecular Diversity Preservation International (MDPI)
Portugal is building a land cover monitoring system to deliver land cover products annually for its mainland territory. This paper presents the methodology developed to produce a prototype relative to 2018 as the first land cover map of the future annual map series (COSsim). A total of thirteen land cover classes are represented, including the most important tree species in Portugal. The mapping approach developed includes two levels of spatial stratification based on landscape dynamics. Strata are analysed independently at the higher level, while nested sublevels can share data and procedures. Multiple stages of analysis are implemented in which subsequent stages improve the outputs of precedent stages. The goal is to adjust mapping to the local landscape and tackle specific problems or divide complex mapping tasks in several parts. Supervised classification of Sentinel-2 time series and post-classification analysis with expert knowledge were performed throughout four stages. The overall accuracy of the map is estimated at 81.3% (±2.1) at the 95% confidence level. Higher thematic accuracy was achieved in southern Portugal, and expert knowledge significantly improved the quality of the map.
Land cover mapping at national scale with Sentinel-2 and LUCAS: a case study in Portugal
Publication . Benevides, Pedro José; Silva, Nuno; Costa, Hugo; Moreira, Francisco D.; Moraes, Daniel; Castelli, Mauro; Caetano, Mário; NOVA Information Management School (NOVA IMS); Information Management Research Center (MagIC) - NOVA Information Management School
Experiments were carried out to investigate the use of Land Use and Coverage Area frame Survey (LUCAS) dataset and Sentinel-2 imagery to produce a land cover map in Portugal through automated supervised classification. LUCAS is a free land cover land use (LCLU) dataset based in Europe, while Sentinel-2 satellites provide also free images with short revisit frequency. The goal was to evaluate if LUCAS dataset from 2018 can be used as a single reference dataset for land cover classification at national level. The Random Forest (RF) algorithm was used. Some processing steps were undertaken to use LUCAS as reference dataset. The original LUCAS LCLU nomenclature was modified into a new nomenclature composed of 12 and 6 level-2 and level-1 map classes, respectively. Filtering was performed on LUCAS metadata, reducing the initial number of LUCAS points over Portugal from 7168 to 4910. Monthly composites of Sentinel-2 images acquired between October 2017 and September 2018 were used. To reduce the imbalance in LUCAS training points, an oversampling technique based on Synthetic Minority Over-Sampling Technique (SMOTE) was used. An independent validation dataset was produced with 600 points. RF shows an overall accuracy (OA) of 57% for level-2 and 72% for level-1 nomenclatures. When using the oversampling technique, the OA accuracy increases by 3% for level2 and 2% for level-1. The preliminary results of this experiment show that LUCAS dataset used in supervised machine learning classification has potential to produce a reliable land cover map at national scale.
Assessment of the introduction of spatial stratification and manual training in automatic supervised image classification
Publication . Moraes, Daniel; Benevides, Pedro; Costa, Hugo; Moreira, Francisco; Caetano, Mário; NOVA Information Management School (NOVA IMS); Information Management Research Center (MagIC) - NOVA Information Management School
The performance of supervised classification depends on the size and quality of the training data. Multiple studies have used reference datasets to extract training data automatically in an efficient way. However, automatic extraction might be inappropriate for some classes. Furthermore, classes can have distinct spectral characteristics across large areas. Thus, dividing the study area into subregions can be beneficial. This study proposes to assess the impact of the introduction of spatial stratification and manually collected training data on classification performance. Two classifications were conducted with the Random Forest classifier and multi-temporal Sentinel-2 data. The classifications’ performance was evaluated by accuracy metrics and visual inspection of the maps. The results indicate that introducing spatial stratification and manual training yielded a higher overall accuracy (66.7%) when compared to the accuracy of a benchmark classification (60.2%) conducted without stratification and with training data collected exclusively by automatic methods. Visual inspection of the maps also revealed some advantages of the novel approach, namely constraining some land cover classes to be present only within specific strata, which avoids commission errors of the class to spread freely across the map. Most of the classification improvements were observed in subregions with specific landscapes and spectral patterns, although these strata represent a small fraction of the study area, which might have contributed to the small increase in accuracy.
Annual Crop Classification Experiments in Portugal Using Sentinel-2
Publication . Benevides, Pedro; Costa, Hugo; Moreira, Francisco D.; Moraes, Daniel; Caetano, Mario; NOVA Information Management School (NOVA IMS); Information Management Research Center (MagIC) - NOVA Information Management School
This paper presents an experimental crop classification of the 10 most abundant annual crop types in Portugal, using a study area located in Alentejo region. This region has great diversity of land uses as well as multiple crop types. Sentinel-2 2018 intra-annual time-series imagery is considered in the experiment. The Portuguese Land Parcel Identification System (LPIS) is used to extract automatic training samples. LPIS information is automatically processed with the help of auxiliary datasets to filter out crop areas more likely to have been mislabeled. Classification is obtained using random forest. Validation is performed using an independent dataset also based on LPIS. A global accuracy of 76% is obtained. The novelty of the methodology here presented shows that LPIS can be used together with auxiliary data for crop type mapping, helping to characterize the agriculture land diversity in Portugal.
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Entidade financiadora
Fundação para a Ciência e a Tecnologia
Programa de financiamento
3599-PPCDT
Número da atribuição
PCIF/MOS/0046/2017